机器学习
在机器学习中，概率分类器是一种能够根据对输入的观察，预测一组类别的概率分布的分类器，而不仅仅是输出观察最可能属于的类别。概率分类器提供的分类结果本身就很有用，或者在将多个分类器组合成集成模型时也能发挥作用。
神经网络（尤其是多层感知器 MLP）经常受到一个批评，那就是不清楚它到底在做什么：虽然我们可以查看神经元的激活值和权重，但这些并不能告诉我们太多。
在本主题（概率分类器）中，我们将研究基于统计学的方法。这些方法更具透明性，因为我们总能提取和查看概率，从而了解其含义，而不必担心那些没有明显意义的权重。
In machine learning, a probabilistic classifier is a classifier that is able to predict, given an
observation of an input, a probability distribution over a set of classes, rather than only outputting the
most likely class that the observation should belong to. Probabilistic classifiers provide classification
that can be useful in its own right or when combining classifiers into ensembles.
One criticism that is often made of neural networks—especially the MLP—is that it is not clear exactly
what it is doing: while we can go and have a look at the activations of the neurons and the weights, they
don’t tell us much.
In this topic (probabilistic classifier ) we are going to look at methods that are based on statistics, and
that are therefore more transparent, in that we can always extract and look at the probabilities and see
what they are, rather than having to worry about weights that have no obvious meaning.